om Today, I'm going to attempt to present an argument in favor of a theory that has resulted from my studies relating to AI. While this is one of the only things I have to show for my time spent on AI. I am reasonably confident in it's validity and hope to show why that is the case here.
Unfortunately, the implications of this theory are quite dramatic making the saying "extraordinary claims require extraordinary proof" central to the meditations leading to this posting. I will take this theory and then apply it to recent news articles and make the even bolder claim that AI has been SOLVED, and that the only thing that remains to be done is to create a complete AI agent from the available components. om It can be agreed upon that for something to be intelligent, it must be able to process information symbolically, it must be able to speak. This may be confusing because computers are already thought of as symbolic machines which process formal symbolic languages. An AI system, however, processes *informal* symbolic languages. While it can be argued that this is a requirement for all intelligent systems, it is inescapably true when one talks about superhuman artificial intelligence because without it, it would necessarily be sub-human. It should also be noted that normal humans, through meditation techniques, can achieve an asymbolic state of consciousness, this can be quite enjoyable, especially while driving. But humans incapable of symbolic thought, most notably autistic patients, are not really intelligent. Having established the necessity of symbolic thought, it can then be inferred that an AI system is a system which must support symbolic thought, that is the ability to see a duck and call it a duck. A duck can be photographed in a practically infinite number of ways, cross the number of angles, with the number of distances, with the number of lighting environments, with the number of backgrounds, with the number of species of duck, and you get many many many patterns which all satisfy the category "duck". Any system which can achieve reasonably good performance at this task, must have a concept of "duck" which is separate from any specific instance. When we look at the information flow through an AI which thinks symbolically, we find that it is not even useful to record every pixel of every frame it has ever been shown. In fact, such designs invariably lead to a combinatorial explosion which is computationally intractable. Even more, such information is not even useful because it will never perfectly match any future perception. Instead, the AI must always be searching for information which can be captured by symbols, (though not necessarily words!). Each new perception will almost certainly contain many elements which have previously been assigned symbols by the AI, and some that it hasn't seen before which may or may not be useful to remember. A tabula rasa symbolic intelligence, such as a human baby, for the most part, cannot assign symbols to things in its environment because it doesn't have any. Instead, it must isolate elements from each perception and attempt to use them to explain the next perception. Through trial and error, it will acquire a set of perceptions which "just work". By the age of four or so, a child can deal with, intellectually at least, just about any environment. The bottom line, though, is that because we don't have anything else, our entire intellect is based on relating everything we encounter to things we have previously encountered. Okay, we have a system which extracts symbolic information from perceptions, and analyzes new perceptions with previously acquired symbols. A somewhat less important point is that symbols don't necessarily need to relate to raw perceptions, they can also relate to other symbols or a mixture of symbols and perceptions in a somewhat-heirarchical manner. These observations may seem to be a bit simplistic but they lead to some startling conclusions. Once again, whenever a perception is presented to an AI, it is parsed and split up into old information (satisfying existing symbols), and new information (not satisfying any symbols and therefore requiring further processing). Over time, as an AI builds up an ever increasing library of symbols, it becomes ever more efficient at analyzing new scenes. There is extensive anatomical and physiological evidence to support this. We can also conclude that there exists a growth function which determines the approximate quantity of new information in any given perception, which is on the order of 1/x. We can sum the total of this new information over all perceptions from the first onwards, and find that it is on the order of 1+ log(X), or simply O(X) = Log(X). If we were to present the AI with random information and forced it to remember all of it, the *WORST*, case for AI is O(N). For constant input, the AI will remain static, at O(N) = 1. (these are space complexities). Okay, what about the algorithmic complexity? Lets give the AI a perception, it breaks the perception down into symbols, it must now match the incoming symbols with it's existing library. We know it's existing library is on the order of O(N) = log N for complex, non-random input. If we were to do a basic search on that, the time complexity will be M(symbols) times it's library on the order of log N. Now, if we had a way to sort this library, we could do even better, the search time would be M * log^2(N). that is the logarithm of the logarithm of N. If the information is only partially orderable then the performance will be somewhere in between those upper and lower bounds. Alternatively, if memory weren't an issue we could attempt a hashing algorithm and achieve a performance close to O(1), which is approximately what the human brain does hence it's renown performance in this area. My final claim for this part of the meditation, is that every possible strong AI architecture will match these characteristics. We could attach a tivo to the AI and make a few other compromises and see the AI's performance approximate N + log(N/2), but the point still stands, Every strong AI will be a symbolic AI, otherwise it wouldn't be able to think in any useful manner, Every symbolic AI will encode new perceptions in terms of symbols derived from previous perceptions, and the average quantity of new information in each new perception will approximate the logarithm of the number of previous perceptions. So, therefore, every possible implementation of a successful strong AI will also have the property of lagarithmic growth with respect to the number of new perceptions. The scope of algorithmic complexity also ranges from slow yet practical to ideal (O(1)). An argument that motor-modalities are equivalent to sensory modalities is beyond the scope of what I have the time or space to discuss here. This discovery can be used as a razor for evaluating AI projects. For example, anyone demanding a supercomputer to run their AI, obviously is barking up the wrong tree. Similarly, anyone trying to simulate a billion-node neural network is effectively praying for pixie dust to emerge from the machine and rescue them from their own lack of understanding. We have others who have their heads rammed up their own friendly asses but they aren't worth mentioning. Truly, when one finishes this massacre, the field of AI is left decimated and nearly extinct. -- nearly... On the other hand, when you use this razor to evaluate projects which ostensibly have nothing to do with AI, things become extremely interesting. http://techon.nikkeibp.co.jp/english/NEWS_EN/20070725/136751/ om! I'm out of thyme for the nite, I desperately need my sleap (where I leap into bed and start snoring). It took me all weekend to complete my meditations in preparation for writing this...) I plan to follow this up with a discussion of recent developments in AI and the crisis the singularitarian community has sleep-walked it's way into. -- Opera: Sing it loud! :o( )>-< ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=4007604&id_secret=26439743-9243ae
